Project Proposal Summary

The Team

  • Junjie Hu (jh4617)
  • Ruixi Li (rl3328)
  • Xuan Lu (xl3214)
  • Tianyun Jiang (tj2519)
  • Qinting Shen (qs2261)

Tentative Project Title

HIV/AIDS Surveillance Data Analysis across Countries

Motivation

The primary goal is to analyze information on HIV prevalence, incidence rates, and AIDS mortality from available studies, identifying the most vulnerable gender, sub-population, and geographic region.

Intended Final Products

Multiple dashboards/web pages reflecting the prevalence and incidence rates of HIV across countries and subgroups.

Anticipated Data Sources

United States Census’s HIV/AIDS Surveillance Data Base

Data Cleaning and Preparation

# Incidence

incidence <- read.csv("surveillance/data/hiv_incidence.csv") |>
  janitor::clean_names() |>
  select(-c(no_cases,no_deaths,prev_rate)) |>
  rename(subpop = population_subgroup) |>
  mutate(geographic_area = ifelse(
    str_detect(geographic_area,"rural"),"rural", 
    ifelse(
      str_detect(geographic_area,"semi"),"semiurban",
    ifelse(
      str_detect(geographic_area,"urban"),"urban", geographic_area
    ))))

## recode subpopulation

# sexually active, TB, STI : other non-representative
# pregnant women: other patients
# sex worker&clients: ?
# Patients:?
# HIV+ individuals:?
incidence = incidence |>
  mutate(
    subpop_pooled = ifelse(
     str_detect(subpop, "(?i)police|military"), "Military/Armed Forces",
     ifelse(
       str_detect(subpop, "(?i)child|pediatric"), "Children",
     ifelse(
       str_detect(subpop, "(?i)drug|IVDU") & !str_detect(subpop, "(?i)STI|homo|prisoner|partner|sex worker"),  "Intravenous Drug Users/Needle Sharers",
     ifelse(
       str_detect(subpop, "(?i)sex worker|bar") & !str_detect(subpop,"(?i)trans|IVDU|homo|MSM|drug|client"),"Sex workers",
     ifelse(
       str_detect(subpop, "(?i)transgender|homo|MSM|gay|TB|STI|sexually|prisoner|infants born|HIV2+|high risk|of HIV") & !str_detect(subpop, "(?i)sex worker|IVDU|drug|Testing center attendees"), "Other Non-Representative",
      ifelse(
       str_detect(subpop, "(?i)pts.|pregnant|Mothers") & !str_detect(subpop, "(?i)sex worker|IVDU|STI|homo|TB"), "Other Patients",
      ifelse(
       str_detect(subpop, "(?i)Blood|port|Testing center attendees|employer|contractor|textile|Wives|HIV-") , "General Population","Two Known Mixed Groups"))))))))

# unique(incidence$subpop[str_detect(incidence$subpop,"patient")]) for categorization
# unique(incidence$subpop[incidence$subpop_pooled=="Two Known Mixed Groups"])
incidence = incidence |>
  mutate(subpop_pooled = ifelse(
    subpop %in% c("Bisexuals","Clients of sex workers","High risk individuals","Partners of drug users","Blood transfusion recipients"),"Other Non-Representative",subpop_pooled),
    subpop_pooled = ifelse(
      subpop == "Sex workers & clients","Sex workers",subpop_pooled),
    subpop_pooled = ifelse(
      subpop == "Patients","Other Patients",subpop_pooled),
    subpop_pooled = ifelse(
      subpop %in% c("Heterosexuals","Adults","Individuals","Women","Various groups","Residents","Unspecified population","Truck drivers","Others","Adults - circumcised","Adults - uncircumcised","Factory workers", "General population", "Low risk groups","Fishermen","Workers","Esquineros","Trucking companies employees","Youths","Hospitality girls","Adolescents","Volunteers","Controls","Rural population","Employees","Commercial bank employees"),"General Population",subpop_pooled))
incidence |>filter(subpop == "Patients")
##   sequence country_code      geographic_area reference_date   subpop
## 1        4          CHN Liangshan Prefecture           2011 Patients
## 2        6          CHN Liangshan Prefecture           2013 Patients
## 3       10          CHN Liangshan Prefecture           2013 Patients
## 4        5          CHN Liangshan Prefecture           2012 Patients
## 5        8          TWN               Taiwan      2005-2010 Patients
## 6        7          TWN               Taiwan      2005-2010 Patients
##   subpop_code sex age source_id
## 1           L   B ALL     W0798
## 2           L   B ALL     W0798
## 3           L   B ALL     L1138
## 4           L   B ALL     W0798
## 5           L   B ALL     L1186
## 6           L   B ALL     L1186
##                                                                                                                   comments
## 1 Provider-initiated HIV testing & counseling.  Included blood/blood products recipients, surgical & other pts.  BED-CEIA.
## 2 Provider-initiated HIV testing & counseling.  Included blood/blood products recipients, surgical & other pts.  BED-CEIA.
## 3                                                                                  Located in Sichuan Province.  BED-CEIA.
## 4 Provider-initiated HIV testing & counseling.  Included blood/blood products recipients, surgical & other pts.  BED-CEIA.
## 5                                                                                               Pts. w/ out Herpes Zoster.
## 6                                                                                                   Pts. w/ Herpes Zoster.
##   data_type         country            site_name
## 1         I China, Mainland Liangshan Prefecture
## 2         I China, Mainland Liangshan Prefecture
## 3         I China, Mainland Liangshan Prefecture
## 4         I China, Mainland Liangshan Prefecture
## 5         I   China, Taiwan               Taiwan
## 6         I   China, Taiwan               Taiwan
##                                      author year
## 1         Wang, Q., Y. Yao, S. Yang, et al. 2017
## 2         Wang, Q., Y. Yao, S. Yang, et al. 2017
## 3       Liang, P., Y. Gong, Q. Liao, et al. 2016
## 4         Wang, Q., Y. Yao, S. Yang, et al. 2017
## 5 Lee, Y., O. N. Nfor, D. M. Tantoh, et al. 2015
## 6 Lee, Y., O. N. Nfor, D. M. Tantoh, et al. 2015
##                                                                                                                  title
## 1 Estimation of HIV-1 Incidence with BED-CIAE among Clinical Patients in Liangshan Yi Autonomous Prefecture: 2011-2013
## 2 Estimation of HIV-1 Incidence with BED-CIAE among Clinical Patients in Liangshan Yi Autonomous Prefecture: 2011-2013
## 3   Estimation of HIV-1 Incidence with BED-CEIA among Multiple Populations in Liangshan Yi Autonomous Prefecture: 2013
## 4 Estimation of HIV-1 Incidence with BED-CIAE among Clinical Patients in Liangshan Yi Autonomous Prefecture: 2011-2013
## 5                                    Herpes Zoster as a Predictor of HIV Infection in Taiwan: A Population-Based Study
## 6                                    Herpes Zoster as a Predictor of HIV Infection in Taiwan: A Population-Based Study
##                                                                      publication_information
## 1                         Chinese Journal of AIDS and STD, vol. 23, no. 5, pp. 402-404, 420.
## 2                         Chinese Journal of AIDS and STD, vol. 23, no. 5, pp. 402-404, 420.
## 3                                 Modern Preventive Medicine, vol. 43, no. 9, pp. 1675-1678.
## 4                         Chinese Journal of AIDS and STD, vol. 23, no. 5, pp. 402-404, 420.
## 5 PLoS One, vol. 10, no. 11, e0142254, <http://www.ploseone.org>, accessed on July 12, 2016.
## 6 PLoS One, vol. 10, no. 11, e0142254, <http://www.ploseone.org>, accessed on July 12, 2016.
##   virus_type inc_rate specimen_type test_type sampsize  subpop_pooled
## 1       HIV1     0.17             B     ELISA    1,739 Other Patients
## 2       HIV1     0.31             B     ELISA    1,948 Other Patients
## 3        HIV     0.31             B     ELISA    1,948 Other Patients
## 4       HIV1     0.41             B     ELISA    1,731 Other Patients
## 5        HIV     0.01             B       UNK      N/A Other Patients
## 6        HIV     0.02             B       UNK      N/A Other Patients
incidence |> filter(str_detect(title,"Estimation of HIV-1 Incidence with BED-CIAE among Clinical Patients in Liangshan Yi Autonomous Prefecture"))
##   sequence country_code      geographic_area reference_date   subpop
## 1        4          CHN Liangshan Prefecture           2011 Patients
## 2        6          CHN Liangshan Prefecture           2013 Patients
## 3        5          CHN Liangshan Prefecture           2012 Patients
##   subpop_code sex age source_id
## 1           L   B ALL     W0798
## 2           L   B ALL     W0798
## 3           L   B ALL     W0798
##                                                                                                                   comments
## 1 Provider-initiated HIV testing & counseling.  Included blood/blood products recipients, surgical & other pts.  BED-CEIA.
## 2 Provider-initiated HIV testing & counseling.  Included blood/blood products recipients, surgical & other pts.  BED-CEIA.
## 3 Provider-initiated HIV testing & counseling.  Included blood/blood products recipients, surgical & other pts.  BED-CEIA.
##   data_type         country            site_name
## 1         I China, Mainland Liangshan Prefecture
## 2         I China, Mainland Liangshan Prefecture
## 3         I China, Mainland Liangshan Prefecture
##                              author year
## 1 Wang, Q., Y. Yao, S. Yang, et al. 2017
## 2 Wang, Q., Y. Yao, S. Yang, et al. 2017
## 3 Wang, Q., Y. Yao, S. Yang, et al. 2017
##                                                                                                                  title
## 1 Estimation of HIV-1 Incidence with BED-CIAE among Clinical Patients in Liangshan Yi Autonomous Prefecture: 2011-2013
## 2 Estimation of HIV-1 Incidence with BED-CIAE among Clinical Patients in Liangshan Yi Autonomous Prefecture: 2011-2013
## 3 Estimation of HIV-1 Incidence with BED-CIAE among Clinical Patients in Liangshan Yi Autonomous Prefecture: 2011-2013
##                                              publication_information virus_type
## 1 Chinese Journal of AIDS and STD, vol. 23, no. 5, pp. 402-404, 420.       HIV1
## 2 Chinese Journal of AIDS and STD, vol. 23, no. 5, pp. 402-404, 420.       HIV1
## 3 Chinese Journal of AIDS and STD, vol. 23, no. 5, pp. 402-404, 420.       HIV1
##   inc_rate specimen_type test_type sampsize  subpop_pooled
## 1     0.17             B     ELISA    1,739 Other Patients
## 2     0.31             B     ELISA    1,948 Other Patients
## 3     0.41             B     ELISA    1,731 Other Patients
check=incidence |>
  filter(subpop_pooled != "Two Known Mixed Groups") |>
  group_by(subpop_pooled,subpop) |>
  summarise(n=n())
## `summarise()` has grouped output by 'subpop_pooled'. You can override using the
## `.groups` argument.
incidence|>
  filter(subpop_pooled == "Two Known Mixed Groups") 
##    sequence country_code    geographic_area reference_date
## 1         4          ARG        Five cities      2006-2009
## 2         9          AUS          Melbourne      2007-2013
## 3        21          CHN             Urumqi      2002-2003
## 4        18          CHN             Urumqi      2002-2003
## 5         7          CHN            Kunming      2009-2011
## 6        22          CHN            Lincang           2011
## 7         9          CHN            Kaiyuan      2006-2013
## 8        11          CHN            Tianjin      2011-2013
## 9        10          CHN            Xichang      2002-2005
## 10        2          CHN            Kaiyuan      2006-2013
## 11        8          CHN             Urumqi           2003
## 12        6          CHN             Urumqi           2001
## 13        6          CHN            Kunming      2009-2011
## 14        6          CHN            Beijing      2006-2008
## 15       20          CHN             Urumqi      2002-2003
## 16       13          CHN            Tianjin           2008
## 17       63          IRN         27 prisons           2013
## 18       41          IRN          10 cities           2014
## 19       52          IRN         27 prisons           2009
## 20       50          IRN         27 prisons           2009
## 21       30          IRN          10 cities           2010
## 22       28          IRN          10 cities           2010
## 23       26          IRN          10 cities           2010
## 24       19          IRN          13 cities           2015
## 25        8          IRN          13 cities           2010
## 26        6          IRN          13 cities           2010
## 27       17          IRN          13 cities           2015
## 28       37          IRN          10 cities           2014
## 29       39          IRN          10 cities           2014
## 30       65          IRN         27 prisons           2013
## 31        3          KAZ Almaty & Termirtau      2015-2018
## 32        4          KAZ Almaty & Termirtau      2015-2018
## 33        9          NGA      Abuja & Lagos      2013-2018
## 34       12          NGA      Abuja & Lagos      2013-2018
## 35        8          RUS     St. Petersburg      2009-2010
## 36        7          RUS     St. Petersburg      2009-2010
## 37        9          THA            Bangkok      2005-2010
## 38        8          THA            Bangkok      2005-2010
## 39       14          THA            Bangkok      2006-2012
## 40        8          THA            Bangkok      2001-2002
## 41       15          THA            Bangkok      2006-2012
## 42        9          THA            Bangkok      2006-2012
## 43       17          TWN             Taiwan      2006-2010
## 44       14          TWN             Taiwan           2010
## 45       13          TWN             Taiwan           2009
## 46       12          TWN             Taiwan           2008
## 47       11          TWN             Taiwan           2007
## 48       10          TWN             Taiwan           2006
## 49        9          TWN             Taiwan           2005
## 50        8          TWN             Taiwan           2004
## 51       11          TZA      Dar es Salaam           2019
## 52       11          UGA            Kampala      2008-2017
## 53        7          UGA            Kampala      2009-2011
##                                    subpop subpop_code sex age source_id
## 1          Male sex workers - transgender           P   M ALL     F0365
## 2              IVDU homosexuals/bisexuals           I   M ALL     C1449
## 3                        IVDU sex workers           I   F ALL     Z0286
## 4                        IVDU sex workers           I   B ALL     Z0286
## 5       Drug user homosexuals & bisexuals           I   M ALL     X0129
## 6                  Drug users & prisoners           X   B ALL     Z0575
## 7                   Drug user sex workers           I   F ALL     S1647
## 8                   Drug user homosexuals           I   M ALL     Y0279
## 9                           IVDU STI pts.           I   B ALL     R0558
## 10                  Drug user sex workers           I   F ALL     S1586
## 11   Sex workers & clients of sex workers           X   B ALL     J0182
## 12   Sex workers & clients of sex workers           X   B ALL     J0182
## 13 Male sex workers - homosexual/bisexual           P   M ALL     X0129
## 14          Male sex workers - homosexual           P   M ALL     L0985
## 15                       IVDU sex workers           I   M ALL     Z0286
## 16          Male sex workers - homosexual           P   M ALL     N0643
## 17                    Drug user prisoners           I   B ALL     S1905
## 18                         IVDU prisoners           I   B ALL     S1905
## 19                         IVDU prisoners           I   B ALL     S1905
## 20                    Drug user prisoners           I   B ALL     S1905
## 21                         IVDU prisoners           I   B ALL     S1905
## 22                          IVDU STI pts.           I   B ALL     S1905
## 23                       IVDU homosexuals           I   M ALL     S1905
## 24                       IVDU sex workers           I   F ALL     S1905
## 25                       IVDU sex workers           I   F ALL     S1905
## 26                  Drug user sex workers           I   F ALL     S1905
## 27                  Drug user sex workers           I   F ALL     S1905
## 28                       IVDU homosexuals           I   M ALL     S1905
## 29                          IVDU STI pts.           I   B ALL     S1905
## 30                         IVDU prisoners           I   B ALL     S1905
## 31                  Drug user sex workers           I   F ALL     E0308
## 32                  Drug user sex workers           I   F ALL     E0308
## 33             IVDU homosexuals/bisexuals           I   M ALL     N1055
## 34 Male sex workers - homosexual/bisexual           P   M ALL     N1055
## 35                       IVDU sex workers           I   M ALL     K0992
## 36                       IVDU sex workers           I   B ALL     K0992
## 37                       IVDU homosexuals           I   M ALL     M1599
## 38                         IVDU prisoners           I   B ALL     M1599
## 39                  Drug user homosexuals           I   M ALL     V0323
## 40                         IVDU prisoners           I   M ALL     T0283
## 41          Male sex workers - homosexual           P   M ALL     V0323
## 42                 Male sex workers - MSM           P   M ALL     P0641
## 43             IVDU & drug user prisoners           I   B ALL     H0507
## 44                         IVDU prisoners           I   B ALL     H0507
## 45                         IVDU prisoners           I   B ALL     H0507
## 46                         IVDU prisoners           I   B ALL     H0507
## 47                         IVDU prisoners           I   B ALL     H0507
## 48                         IVDU prisoners           I   B ALL     H0507
## 49                         IVDU prisoners           I   B ALL     H0507
## 50                         IVDU prisoners           I   B ALL     H0507
## 51                  Drug user sex workers           I   F ALL     F0362
## 52                  Drug user sex workers           I   F ALL     K1317
## 53                  Drug user sex workers           I   F ALL     V0410
##                                                                                                                                                                                                                  comments
## 1                                                                                             Cities: Buenos Aires, La Plata, Cordoba, Rosario, & Santiago del Estero.  Oct. 06 - Oct. 09.  STARHS.  Rapid test: Bio-Rad.
## 2                                                                                                       83 person yrs. of observation.  Located in Victoria state.  Men having sex w/ men (MSM).  1 Jan. 07 - 31 Dec. 13.
## 3                                                                                                                          8 person yrs. of observation.  Followup at 6 & 12 mos.  Located in Xinjiang Autonomous Region.
## 4                                                                                        55.5 person yrs. of observation.  Followup at 6 & 12 mos.  Located in Xinjiang Autonomous Region.  Breakdown by sex is provided.
## 5                                                                                                           8.2 person yrs. of observation.  Located in Yunnan Provnce.  Men having sex w/ men (MSM).  June 09 - Mar. 11.
## 6                                                                                                                                                                                  Located in Yunnan Province.  BED-CEIA.
## 7                                                                                                                                                          643.5 person yrs. of observation.  Located in Yunnan Province.
## 8                                                               120.5 person yrs. of observation.  Recruited from a bathhouse.  Men having sex w/ men (MSM).  Apr. 11 - Sept. 13.  Rapid tests: A-WARE, ACON/SD Biolline.
## 9                                                                                                                                                Located in Sichuan Province.  Age 18+.  Pts. w/ syphilis.  Nov. 02 - 05.
## 10                                                                                                                                    120.75 person yrs. of observation.  Located in Yunnan Province.  Mar. 06 - June 13.
## 11                                                                                                                                                         Sentinel surveillance.  Located in Xinjiang Autonomous Region.
## 12                                                                                                                                                         Sentinel surveillance.  Located in Xinjiang Autonomous Region.
## 13                                                                                                         10.4 person yrs. of observation.  Located in Yunnan Provnce.  Men having sex w/ men (MSM).  June 09 - Mar. 11.
## 14                                                                                                                                          4.57 person yrs. of observation.  Men having sex w/ men (MSM).  From Nov. 06.
## 15                                                                                                                      47.5 person yrs. of observation.  Followup at 6 & 12 mos.  Located in Xinjiang Autonomous Region.
## 16                                                                                                                                                         Age range 18-65 yrs.  Apr. - June & Oct. - Dec. 08.  BED-CEIA.
## 17                                                                                                                                                                           144341 person yrs. of observation.  Age 18+.
## 18                                                                                                                                                                            31590 person yrs. of observation.  Age 18+.
## 19                                                                                                                                                                            11591 person yrs. of observation.  Age 18+.
## 20                                                                                                                                                      47126 person yrs. of observation.  Age 18+.  Drug use in past yr.
## 21                                                                                                                                                                            16163 person yrs. of observation.  Age 18+.
## 22                                                                                                                                                                             1810 person yrs. of observation.  Age 18+.
## 23                                                                                                                                               2380 person yrs. of observation.  Age 18+.  Men having sex w/ men (MSM).
## 24                                                                                                                                          1704 person yrs. of observation.  Age 18+.  Rapid tests: Determine & Unigold.
## 25                                                                                                                                                                             2117 person yrs. of observation.  Age 18+.
## 26                                                                                                                                                                             9759 person yrs. of observation.  Age 18+.
## 27                                                                                                                                         15395 person yrs. of observation.  Age 18+.  Rapid tests: Determine & Unigold.
## 28                                                                                                                                               5469 person yrs. of observation.  Age 18+.  Men having sex w/ men (MSM).
## 29                                                                                                                                                                             3289 person yrs. of observation.  Age 18+.
## 30                                                                                                                                                                           31394 person yrs. of observation.  Age 18+ .
## 31                                                Only the incidence rate was given.  153.3 person yrs. of observation.  Combination HIV Risk Reduction (HIVRR) arm.  6 & 12 mos. follow up.  Age 19+.  May 15 - Oct. 18.
## 32                             Only the incidence rate was given.  153.3 person yrs. of observation.  Combination HIV Risk Reduction - Micro Finance (HIVRR-MF) arm.  6 & 12 mos. follow up.  Age 19+.  May 15 - Oct. 18.
## 33                                                                         Only the incidence rate was given.  6 person yrs. of observation.  Age 16+.  Mar. 13 - Mar. 18.  Rapid tests: Determine, Uni-Gold, & Stat Pak.
## 34                              Only the incidence rate was given.  180 person yrs. of observation.  Had sex in exchange for money or gifts.  Age 16+.  Mar. 13 - Mar. 18.  Rapid tests: Determine, Uni-Gold, & Stat Pak.
## 35                                                                                                                                                                                                                       
## 36                                                                                                                                                                                                                       
## 37                                                                                                                   112 person yrs. of observation.  Sites: 17 drug treatment centers.  Since June 05.  Also, see C1290.
## 38                                                                                                                  1026 person yrs. of observation.  Sites: 17 drug treatment centers.  Since June 05.  Also, see C1290.
## 39 253 PYO.  Men having sex w/ men (MSM).  Recruited from HIV testing services & entertainment venues: bars, discos, & saunas.  At baseline.  Rapid tests: OraQuick, Determine, Double Check/SD-Bioline, & Capillus/Core.
## 40                                                                                 81.08 person yrs. of observation.  Klong Prem Central Prison Medical Correctional Institution.  Followed up 5 mos.  June 01 - Aug. 02.
## 41 310 PYO.  Men having sex w/ men (MSM).  Recruited from HIV testing services & entertainment venues: bars, discos, & saunas.  At baseline.  Rapid tests: OraQuick, Determine, Double Check/SD-Bioline, & Capillus/Core.
## 42                      Only the incidence rate was given.  301 person yrs. of observation.  MSM=Men having sex w/ men.  Age 18+.  6 Apr. 06 - 20 Mar. 12.  Rapid tests: Determine, DoubleCheck or SDBioline, & Capillus.
## 43                                                                                                                                                                                       5670 person yrs. of observation.
## 44                                                                                                                                                                                        1 Jan. - 31 Dec. 10.  BED-CEIA.
## 45                                                                                                                                                                                        1 Jan. - 31 Dec. 09.  BED-CEIA.
## 46                                                                                                                                                                                        1 Jan. - 31 Dec. 08.  BED-CEIA.
## 47                                                                                                                                                                                        1 Jan. - 31 Dec. 07.  BED-CEIA.
## 48                                                                                                                                                                                        1 Jan. - 31 Dec. 06.  BED-CEIA.
## 49                                                                                                                                                                                        1 Jan. - 31 Dec. 05.  BED-CEIA.
## 50                                                                                                                                                                                        1 Jan. - 31 Dec. 04.  BED-CEIA.
## 51                                                                            Only the incidence rate was given.  71 person yrs. of observations.  Respondent-driven sampling (RDS).  Rapid tests: SD Bioline & Uni-Gold.
## 52                                                                                                                 1260 person yrs. of observation.  Mar. 08 - 29 Aug. 17.  Rapid tests: Determine, Stat-Pak, & Uni-Gold.
## 53                                                                                                                                Only the incidence rate was given.  253 person yrs. of observation.  Apr. 08 - Mar. 11.
##    data_type         country                                          site_name
## 1          I       Argentina                                        Five cities
## 2          I       Australia                     Melbourne Sexual Health Centre
## 3          I China, Mainland                                             Urumqi
## 4          I China, Mainland                                             Urumqi
## 5          I China, Mainland                                            Kunming
## 6          I China, Mainland                                            Lincang
## 7          I China, Mainland                                            Kaiyuan
## 8          I China, Mainland                                            Tianjin
## 9          I China, Mainland                                            Xichang
## 10         I China, Mainland                                            Kaiyuan
## 11         I China, Mainland                                             Urumqi
## 12         I China, Mainland                                             Urumqi
## 13         I China, Mainland                                            Kunming
## 14         I China, Mainland                                            Beijing
## 15         I China, Mainland                                             Urumqi
## 16         I China, Mainland                                            Tianjin
## 17         I            Iran                                         27 prisons
## 18         I            Iran                                          10 cities
## 19         I            Iran                                         27 prisons
## 20         I            Iran                                         27 prisons
## 21         I            Iran                                          10 cities
## 22         I            Iran                                          10 cities
## 23         I            Iran                                          10 cities
## 24         I            Iran                                          13 cities
## 25         I            Iran                                          13 cities
## 26         I            Iran                                          13 cities
## 27         I            Iran                                          13 cities
## 28         I            Iran                                          10 cities
## 29         I            Iran                                          10 cities
## 30         I            Iran                                         27 prisons
## 31         I      Kazakhstan                                  Almaty, Termirtau
## 32         I      Kazakhstan                                  Almaty, Termirtau
## 33         I         Nigeria                                       Abuja, Lagos
## 34         I         Nigeria                                       Abuja, Lagos
## 35         I          Russia                                     St. Petersburg
## 36         I          Russia                                     St. Petersburg
## 37         I        Thailand                                           17 sites
## 38         I        Thailand                                           17 sites
## 39         I        Thailand                                            Bangkok
## 40         I        Thailand                          Klong Prem Central Prison
## 41         I        Thailand                                            Bangkok
## 42         I        Thailand                             Silom Community clinic
## 43         I   China, Taiwan                                             Taiwan
## 44         I   China, Taiwan                                             Taiwan
## 45         I   China, Taiwan                                             Taiwan
## 46         I   China, Taiwan                                             Taiwan
## 47         I   China, Taiwan                                             Taiwan
## 48         I   China, Taiwan                                             Taiwan
## 49         I   China, Taiwan                                             Taiwan
## 50         I   China, Taiwan                                             Taiwan
## 51         I        Tanzania Muhimbili University of Health and Allied Sciences
## 52         I          Uganda                                         One clinic
## 53         I          Uganda                                         One clinic
##                                                    author year
## 1      Farias, M. S. R., M. N. Garcia, E. Reynaga, et al. 2011
## 2     Cheung, K. T., C. K. Fairley, T. R. H. Read, et al. 2016
## 3                 Zhang, Y., H. Shan, J. Trizzino, et al. 2007
## 4                 Zhang, Y., H. Shan, J. Trizzino, et al. 2007
## 5                           Xu, J., M. An, X. Han, et al. 2013
## 6                         Zhu, Q., C. Yang, Z. Li, et al. 2015
## 7                   Su, Y., G. Ding, K. H. Reilly, et al. 2016
## 8                        Yu, M., G. Jiang, Z. Dou, et al. 2016
## 9                        Ruan, Y., G. Qin, L. Yin, et al. 2007
## 10                        Su, Y., G. Ding, H. Liu, et al. 2015
## 11                      Joseph, O., M. Zhang, L. J. Zhang 2005
## 12                      Joseph, O., M. Zhang, L. J. Zhang 2005
## 13                          Xu, J., M. An, X. Han, et al. 2013
## 14                        Li, D., Y. Jia, Y. Ruan, et al. 2010
## 15                Zhang, Y., H. Shan, J. Trizzino, et al. 2007
## 16                 Ning, T. L., Y. Guo, Z. Q. Liu, et al. 2011
## 17        Sharifi, H., A. Mirzazadeh, M. Shokoohi, et al. 2018
## 18        Sharifi, H., A. Mirzazadeh, M. Shokoohi, et al. 2018
## 19        Sharifi, H., A. Mirzazadeh, M. Shokoohi, et al. 2018
## 20        Sharifi, H., A. Mirzazadeh, M. Shokoohi, et al. 2018
## 21        Sharifi, H., A. Mirzazadeh, M. Shokoohi, et al. 2018
## 22        Sharifi, H., A. Mirzazadeh, M. Shokoohi, et al. 2018
## 23        Sharifi, H., A. Mirzazadeh, M. Shokoohi, et al. 2018
## 24        Sharifi, H., A. Mirzazadeh, M. Shokoohi, et al. 2018
## 25        Sharifi, H., A. Mirzazadeh, M. Shokoohi, et al. 2018
## 26        Sharifi, H., A. Mirzazadeh, M. Shokoohi, et al. 2018
## 27        Sharifi, H., A. Mirzazadeh, M. Shokoohi, et al. 2018
## 28        Sharifi, H., A. Mirzazadeh, M. Shokoohi, et al. 2018
## 29        Sharifi, H., A. Mirzazadeh, M. Shokoohi, et al. 2018
## 30        Sharifi, H., A. Mirzazadeh, M. Shokoohi, et al. 2018
## 31      El-Bassel, N., T. McCrimmon, G. Mergenova, et al. 2021
## 32      El-Bassel, N., T. McCrimmon, G. Mergenova, et al. 2021
## 33       Nowak, R. G., A. Mitchell, T. A. Crowell, et al. 2019
## 34       Nowak, R. G., A. Mitchell, T. A. Crowell, et al. 2019
## 35 Kozlov, A. P., R. V. Skochilov, O. V. Toussova, et al. 2016
## 36 Kozlov, A. P., R. V. Skochilov, O. V. Toussova, et al. 2016
## 37    Martin, M., S. Vanichseni, P. Suntharasamai, et al. 2014
## 38    Martin, M., S. Vanichseni, P. Suntharasamai, et al. 2014
## 39  van Griensven, F., W. Thienkrua, J. McNicholl, et al. 2013
## 40  Thaisri, H., J. Lerwitworapong, S. Vongsheree, et al. 2003
## 41  van Griensven, F., W. Thienkrua, J. McNicholl, et al. 2013
## 42  Piyaraj, P., P. F. van Griensven, T. H. Holtz, et al. 2018
## 43               Huang, Y., J. Yang, K. E. Nelson, et al. 2014
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## 45               Huang, Y., J. Yang, K. E. Nelson, et al. 2014
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## 47               Huang, Y., J. Yang, K. E. Nelson, et al. 2014
## 48               Huang, Y., J. Yang, K. E. Nelson, et al. 2014
## 49               Huang, Y., J. Yang, K. E. Nelson, et al. 2014
## 50               Huang, Y., J. Yang, K. E. Nelson, et al. 2014
## 51              Faini, D., F. Msafiri, P. Munseri, et al. 2022
## 52            Kasamba, I., S. Nash, M. Shahmanesh, et al. 2019
## 53        Vandepitte, J., H. A. Weiss, J. Bukenya, et al. 2013
##                                                                                                                                                                                                              title
## 1                                                                                 First Report on Sexually Transmitted Infections among Trans (Male to Female Transvestites, Transsexuals, or Transgender) and ...
## 2                                                                                             HIV Incidence and Predictors of Incident HIV among Men Who Have Sex with Men Attending a Sexual Health Clinic in ...
## 3                                                                                       HIV Incidence, Retention Rate, and Baseline Predictors of HIV Incidence and Retention in a Prospective Cohort Study of ...
## 4                                                                                       HIV Incidence, Retention Rate, and Baseline Predictors of HIV Incidence and Retention in a Prospective Cohort Study of ...
## 5                                                                                      Prospective Cohort Study of HIV Incidence and Molecular Characteristics of HIV among Men Who Have Sex with Men (MSM) in ...
## 6                                                                                                       Estimation of Infection Rate of HIV-1 in Different Populations in Lincang City in 2011 with BED-CEIA Assay
## 7                                                                                      Loss to Follow-Up and HIV Incidence in Female Sex Workers in Kaiyuan, Yunnan Province China: A Nine Year Longitudinal Study
## 8                                                                                                        HIV Infection Incidence among Men Who Have Sex with Men in Common Bathing Pool in Tianjin: A Cohort Study
## 9                                                                                 Incidence of HIV, Hepatitis C and Hepatitis B Viruses among Injection Drug Users in Southwestern China: A 3-Year Follow-up Study
## 10                                                                                                        Influencing Factors for Loss to Follow-Up in a Longitudinal Study on HIV Incidence of Female Sex Workers
## 11                                                                                                                                                   Analysis of HIV/AIDS Surveillance in Urumqi from 2000 to 2003
## 12                                                                                                                                                   Analysis of HIV/AIDS Surveillance in Urumqi from 2000 to 2003
## 13                                                                                     Prospective Cohort Study of HIV Incidence and Molecular Characteristics of HIV among Men Who Have Sex with Men (MSM) in ...
## 14                                                                                  Correlates of Incident Infections for HIV, Syphilis, and Hepatitis B Virus in a Cohort of Men Who Have Sex with Men in Beijing
## 15                                                                                      HIV Incidence, Retention Rate, and Baseline Predictors of HIV Incidence and Retention in a Prospective Cohort Study of ...
## 16                                                                                                                      Survey on Recent HIV Infection among Men Who Have Sex with Men in Tianjin during 2008-2009
## 17                                                                                                                                      Estimation of HIV Incidence and Its Trend in Three Key Populations in Iran
## 18                                                                                                                                      Estimation of HIV Incidence and Its Trend in Three Key Populations in Iran
## 19                                                                                                                                      Estimation of HIV Incidence and Its Trend in Three Key Populations in Iran
## 20                                                                                                                                      Estimation of HIV Incidence and Its Trend in Three Key Populations in Iran
## 21                                                                                                                                      Estimation of HIV Incidence and Its Trend in Three Key Populations in Iran
## 22                                                                                                                                      Estimation of HIV Incidence and Its Trend in Three Key Populations in Iran
## 23                                                                                                                                      Estimation of HIV Incidence and Its Trend in Three Key Populations in Iran
## 24                                                                                                                                      Estimation of HIV Incidence and Its Trend in Three Key Populations in Iran
## 25                                                                                                                                      Estimation of HIV Incidence and Its Trend in Three Key Populations in Iran
## 26                                                                                                                                      Estimation of HIV Incidence and Its Trend in Three Key Populations in Iran
## 27                                                                                                                                      Estimation of HIV Incidence and Its Trend in Three Key Populations in Iran
## 28                                                                                                                                      Estimation of HIV Incidence and Its Trend in Three Key Populations in Iran
## 29                                                                                                                                      Estimation of HIV Incidence and Its Trend in Three Key Populations in Iran
## 30                                                                                                                                      Estimation of HIV Incidence and Its Trend in Three Key Populations in Iran
## 31                                                      A Cluster-Randomized Controlled Trial of a Combination HIV Risk Reduction and Microfinance Intervention for Female Sex Workers Who Use Drugs in Kazakhstan
## 32                                                      A Cluster-Randomized Controlled Trial of a Combination HIV Risk Reduction and Microfinance Intervention for Female Sex Workers Who Use Drugs in Kazakhstan
## 33                                                                                                            Individual and Sexual Network Predictors of HIV Incidence among Men Who Have Sex with Men in Nigeria
## 34                                                                                                            Individual and Sexual Network Predictors of HIV Incidence among Men Who Have Sex with Men in Nigeria
## 35                                                                                         HIV Incidence and Behavioral Correlates of HIV Acquisition in a Cohort of Injection Drug Users in St Petersburg, Russia
## 36                                                                                         HIV Incidence and Behavioral Correlates of HIV Acquisition in a Cohort of Injection Drug Users in St Petersburg, Russia
## 37                                                                                    Risk Behaviors and Risk Factors for HIV Infection among Participants in the Bangkok Tenofovir Study, an HIV Pre-Exposure ...
## 38                                                                                    Risk Behaviors and Risk Factors for HIV Infection among Participants in the Bangkok Tenofovir Study, an HIV Pre-Exposure ...
## 39                                                                                                         Evidence of an Explosive Epidemic of HIV Infection in a Cohort of Men Who Have Sex with Men in Thailand
## 40                                                                                                                   HIV Infection and Risk Factors among Bangkok Prisoners, Thailand: A Prospective Cohortr Study
## 41                                                                                                         Evidence of an Explosive Epidemic of HIV Infection in a Cohort of Men Who Have Sex with Men in Thailand
## 42 The Finding of Casual Sex Partners on the Internet, Methamphetamine Use for Sexual Pleasure, and Incidence of HIV Infection among Men Who Have Sex with Men in Bangkok, Thailand: An Observational Cohort Study
## 43                                                                                Changes in HIV Incidence among People Who Inject Drugs in Taiwan Following Introduction of a Harm Reduction Program: A Study ...
## 44                                                                                Changes in HIV Incidence among People Who Inject Drugs in Taiwan Following Introduction of a Harm Reduction Program: A Study ...
## 45                                                                                Changes in HIV Incidence among People Who Inject Drugs in Taiwan Following Introduction of a Harm Reduction Program: A Study ...
## 46                                                                                Changes in HIV Incidence among People Who Inject Drugs in Taiwan Following Introduction of a Harm Reduction Program: A Study ...
## 47                                                                                Changes in HIV Incidence among People Who Inject Drugs in Taiwan Following Introduction of a Harm Reduction Program: A Study ...
## 48                                                                                Changes in HIV Incidence among People Who Inject Drugs in Taiwan Following Introduction of a Harm Reduction Program: A Study ...
## 49                                                                                Changes in HIV Incidence among People Who Inject Drugs in Taiwan Following Introduction of a Harm Reduction Program: A Study ...
## 50                                                                                Changes in HIV Incidence among People Who Inject Drugs in Taiwan Following Introduction of a Harm Reduction Program: A Study ...
## 51                                             The Prevalence, Incidence, and Risk Factors for HIV among Female Sex Workers - A Cohort being Prepared for a Phase IIb HIV Vaccine Trial in Dar es Salaam, Tanzania
## 52                                                       Missed Study Visits and Subsequent HIV Incidence among Women in a Predominantly Sex Worker Cohort Attending a Dedicated Clinic Service in Kampala, Uganda
## 53                                                                                    Alcohol Use, Mycoplasma Genitalium, and Other STIs Associated with HIV Incidence among Women at High Risk in Kampala, Uganda
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##    virus_type inc_rate specimen_type                  test_type sampsize
## 1         HIV    10.70            BW           ELISA, RAPID, WB      259
## 2         HIV     8.43             B                  ELISA, WB      N/A
## 3        HIV1     0.00            BW                  ELISA, WB        8
## 4        HIV1    14.40            BW                  ELISA, WB       57
## 5         HIV     0.00            BW                ELISA*2, WB      N/A
## 6        HIV1     0.21             B                      ELISA       37
## 7        HIV1     2.02            BW                ELISA*2, WB      186
## 8         HIV     6.64            BW                RAPID*2, WB      N/A
## 9         HIV     1.70            BW                  ELISA, WB       44
## 10        HIV     4.14             B                ELISA*2, WB       35
## 11        HIV     0.77             B                        UNK      130
## 12        HIV     0.38             B                        UNK      261
## 13        HIV     0.00            BW                ELISA*2, WB      N/A
## 14        HIV    25.13            BW                  ELISA, WB      N/A
## 15       HIV1    16.80            BW                  ELISA, WB       49
## 16        HIV     4.20            BW                  ELISA, WB      178
## 17        HIV     0.05            BS                    ELISA*2      N/A
## 18        HIV     0.57            BS                    ELISA*2      N/A
## 19        HIV     0.47            BS                    ELISA*2      N/A
## 20        HIV     0.16            BS                    ELISA*2      N/A
## 21        HIV     1.87            BS                    ELISA*2      N/A
## 22        HIV     2.04            BS                    ELISA*2      N/A
## 23        HIV     1.58            BS                    ELISA*2      N/A
## 24        HIV     0.41            BS                    RAPID*2      N/A
## 25        HIV     0.47            BS                    ELISA*2      N/A
## 26        HIV     0.24            BS                    ELISA*2      N/A
## 27        HIV     0.14            BS                    RAPID*2      N/A
## 28        HIV     0.42            BS                    ELISA*2      N/A
## 29        HIV     0.70            BS                    ELISA*2      N/A
## 30        HIV     0.17            BS                    ELISA*2      N/A
## 31        HIV     0.65             B                        UNK      N/A
## 32        HIV     0.00             B                        UNK      N/A
## 33        HIV    54.91             B                    RAPID*3      N/A
## 34        HIV    19.48             B                    RAPID*3      N/A
## 35       HIV1    11.10            BW                  ELISA, WB       17
## 36       HIV1     9.70            BW                  ELISA, WB       19
## 37        HIV     0.00             O                        UNK      N/A
## 38        HIV     1.46             O                        UNK      N/A
## 39        HIV    11.50            BW                    RAPID*4      N/A
## 40       HIV1    11.10             B                  ELISA, WB      351
## 41        HIV     7.40            BW                    RAPID*4      N/A
## 42        HIV     7.31            BW                    RAPID*3      N/A
## 43        HIV     0.00            BW                        UNK      N/A
## 44        HIV     0.27            BW                      ELISA       64
## 45        HIV     0.29            BW                      ELISA      107
## 46        HIV     0.85            BW                      ELISA      244
## 47        HIV     1.84            BW                      ELISA      545
## 48        HIV    11.58            BW                      ELISA    1,413
## 49        HIV    18.16            BW                      ELISA    1,363
## 50        HIV     6.44            BW                      ELISA      115
## 51        HIV     4.25            BW           RAPID*2, ELISA*2      N/A
## 52        HIV     3.81             B RAPID, ELISA*2, WB/RAPID*3      N/A
## 53       HIV1     4.35            BW                    ELISA*2      N/A
##             subpop_pooled
## 1  Two Known Mixed Groups
## 2  Two Known Mixed Groups
## 3  Two Known Mixed Groups
## 4  Two Known Mixed Groups
## 5  Two Known Mixed Groups
## 6  Two Known Mixed Groups
## 7  Two Known Mixed Groups
## 8  Two Known Mixed Groups
## 9  Two Known Mixed Groups
## 10 Two Known Mixed Groups
## 11 Two Known Mixed Groups
## 12 Two Known Mixed Groups
## 13 Two Known Mixed Groups
## 14 Two Known Mixed Groups
## 15 Two Known Mixed Groups
## 16 Two Known Mixed Groups
## 17 Two Known Mixed Groups
## 18 Two Known Mixed Groups
## 19 Two Known Mixed Groups
## 20 Two Known Mixed Groups
## 21 Two Known Mixed Groups
## 22 Two Known Mixed Groups
## 23 Two Known Mixed Groups
## 24 Two Known Mixed Groups
## 25 Two Known Mixed Groups
## 26 Two Known Mixed Groups
## 27 Two Known Mixed Groups
## 28 Two Known Mixed Groups
## 29 Two Known Mixed Groups
## 30 Two Known Mixed Groups
## 31 Two Known Mixed Groups
## 32 Two Known Mixed Groups
## 33 Two Known Mixed Groups
## 34 Two Known Mixed Groups
## 35 Two Known Mixed Groups
## 36 Two Known Mixed Groups
## 37 Two Known Mixed Groups
## 38 Two Known Mixed Groups
## 39 Two Known Mixed Groups
## 40 Two Known Mixed Groups
## 41 Two Known Mixed Groups
## 42 Two Known Mixed Groups
## 43 Two Known Mixed Groups
## 44 Two Known Mixed Groups
## 45 Two Known Mixed Groups
## 46 Two Known Mixed Groups
## 47 Two Known Mixed Groups
## 48 Two Known Mixed Groups
## 49 Two Known Mixed Groups
## 50 Two Known Mixed Groups
## 51 Two Known Mixed Groups
## 52 Two Known Mixed Groups
## 53 Two Known Mixed Groups
# prevalence

prevalence <- read.csv("surveillance/data/hiv_prevalence.csv") |>
  janitor::clean_names() |>
  select(-c(no_cases,no_deaths,inc_rate))|>
  rename(subpop = population_subgroup)

Exploratory Analysis

# Histogram for HIV incidences

incidence |>
  ggplot(aes(x = inc_rate)) + geom_histogram(binwidth = 1, fill = "blue", color = "black")

# add a small constant 0.01 to each value and conduct log transformation

log_inc = log(pull(incidence,inc_rate)+0.01)

incidence |>
  ggplot(aes(x = log_inc)) + geom_histogram(binwidth = 1, fill = "blue", color = "black")

# Histogram for HIV prevalences

POR = log(pull(prevalence,prev_rate)/(1-pull(prevalence,prev_rate)))
## Warning in log(pull(prevalence, prev_rate)/(1 - pull(prevalence, prev_rate))):
## NaNs produced
prevalence |>
  ggplot(aes(x = prev_rate)) + geom_histogram(binwidth = 1, fill = "blue", color = "black")

# add a small constant 0.01 to each value and conduct log transformation

log_prev = log(pull(prevalence,prev_rate)+0.01)

prevalence |>
  ggplot(aes(x = log_prev)) + geom_histogram(binwidth = 1, fill = "blue", color = "black")

tri = (pull(prevalence,prev_rate))^(1/2)





prevalence |>
  ggplot(aes(x = tri)) + geom_histogram(binwidth = 1, fill = "blue", color = "black")

Statistical Testing

# Load necessary libraries
library(broom)  # For tidy statistical summaries

# ANOVA for numeric variables (e.g., age)
aov_prev = aov(prev_rate ~ sex, data = prevalence) |> broom::tidy()
aov_inc = aov(inc_rate ~ sex, data = incidence) |> broom::tidy()
print(glue::glue("\nANOVA for HIV Prevalence Rate by Age:\n"))
## ANOVA for HIV Prevalence Rate by Age:
print(aov_prev)
## # A tibble: 2 × 6
##   term         df     sumsq meansq statistic   p.value
##   <chr>     <dbl>     <dbl>  <dbl>     <dbl>     <dbl>
## 1 sex           2    18776.  9388.      37.0  8.93e-17
## 2 Residuals 61023 15493264.   254.      NA   NA
print(glue::glue("\nANOVA for HIV Incidence Rate by Age:\n"))
## ANOVA for HIV Incidence Rate by Age:
print(aov_inc)
## # A tibble: 2 × 6
##   term         df  sumsq meansq statistic     p.value
##   <chr>     <dbl>  <dbl>  <dbl>     <dbl>       <dbl>
## 1 sex           2   555.  278.       12.3  0.00000455
## 2 Residuals  3726 83838.   22.5      NA   NA
# t test: Difference in HIV incidence rate between the circumcised and uncircumcised 

circumcise = 
  incidence |>
  mutate(
    subpop = recode(subpop,
                                 'Adults - circumcised' = 'circumcised',
                                 'Adults - uncircumcised' = 'uncircumcised')) |>
  filter(subpop %in% c('circumcised','uncircumcised'))

ttest_result = t.test(inc_rate ~ subpop, data = circumcise) |> broom::tidy()
print(glue::glue("\nT-Test for HIV incidence by circumcision status among adults:\n"))
## T-Test for HIV incidence by circumcision status among adults:
print(ttest_result)
## # A tibble: 1 × 10
##   estimate estimate1 estimate2 statistic p.value parameter conf.low conf.high
##      <dbl>     <dbl>     <dbl>     <dbl>   <dbl>     <dbl>    <dbl>     <dbl>
## 1    -1.24      1.10      2.34     -2.16  0.0397      26.7    -2.42   -0.0631
## # ℹ 2 more variables: method <chr>, alternative <chr>

According to the results of ttest, we have sufficient evidence to conclude that there’s difference in incidence rate between the circumcised and uncircumcised adults. the uncircumcised adults are more likely to get HIV compared with the circumcised adults.

  • Chi-Square Test: Used for testing relationships between categorical variables.
  • ANOVA: Analyzes differences between group means and their associated procedures.
  • T-Test: Compares the means of two groups.
  • Linear Regression: Models the relationship between a dependent variable and one or more independent variables.

Validation of Data:

  • Ensure hiv_df_filtered contains the necessary columns (gender, new_hiv_incidences, aids_mortality, age, age_group, geographic_region).
  • Validate the assumptions for each statistical test (e.g., normality for t-test and ANOVA, independence for Chi-square).

Visualization

library(plotly)
## 
## Attaching package: 'plotly'
## The following object is masked from 'package:ggplot2':
## 
##     last_plot
## The following object is masked from 'package:stats':
## 
##     filter
## The following object is masked from 'package:graphics':
## 
##     layout
library(ggplot2)

# A map reports the HIV prevalence and incidence rates in each country
map_inc = incidence |>
  plot_ly(type = 'choropleth', locations = ~country_code,
               z = ~inc_rate, text = ~paste(country, ': ', inc_rate, '%'),
               hoverinfo = 'text',color = ~inc_rate, colors = "Reds") |>
  layout(title = 'HIV Incidence Rates by country',
         geo = list(projection = list(type = 'orthographic')))

map_inc
map_prev = prevalence |>
  plot_ly(type = 'choropleth', locations = ~country_code,
               z = ~prev_rate, text = ~paste(country, ': ', prev_rate, '%'),
               hoverinfo = 'text',color = ~prev_rate, colors = "Reds") |>
  layout(title = 'HIV Prevalence Rates by country',
         geo = list(projection = list(type = 'orthographic')))
map_prev
# HIV Incidence Rates by circumcision status among adults

circumcise_plot = circumcise |>
  plot_ly(x = ~subpop, y = ~inc_rate, type = 'box') |>
  layout(title = 'HIV Incidence Rates by circumcision status among adults',
         yaxis = list(title = 'Incidence Rate(%)'),
         xaxis = list(title = 'Circumcision Status'))

circumcise_plot
# Difference in HIV Prevalence and Incidence Rates Across Gender
prev_sex = prevalence |>
  group_by(sex) |>
  summarize(median=median(prev_rate), n = n()) |>
  mutate(text_label = str_c("Sex:", sex, " \nMedian:", median, " Sample Size:", n))

sex_plot = incidence |>
  group_by(sex) |>
  summarise(median = median(inc_rate), n = n()) |>
  mutate(text_label = str_c("Sex:", sex, " \nMedian:", median, " Sample Size:", n)) |>
  plot_ly(x = ~sex, y = ~median, size = ~n, type = 'scatter', mode = 'markers', name = 'Incidence Rate',text = ~text_label, hoverinfo = 'text') |>
  add_trace(data = prev_sex, x = ~sex, y = ~median, size = ~n, type = 'scatter', name = 'Prevalence Rate',text = ~text_label, hoverinfo = 'text') |>
  layout(title = 'Median Incidence and Prevalence Rates by Sex',
         xaxis = list(title = 'Sex'),
         yaxis = list(title = 'Median Rate'))

print(sex_plot)
## Warning: `line.width` does not currently support multiple values.

## Warning: `line.width` does not currently support multiple values.
# HIV Prevalence and Incidence Rates by Country

inc_country = incidence |>
  group_by(country) |>
  summarize(median = median(inc_rate)) |>
  mutate(country = fct_reorder(country, median)) |>
  plot_ly(x = ~country, y = ~median, type = 'scatter', mode = 'markers') |> 
  layout(title = 'HIV Incidence Rates by Country',
         yaxis = list(title = 'Incidence Rate(%)'))
inc_country
prev_country = prevalence |>
  group_by(country) |>
  summarize(median = median(prev_rate)) |>
  mutate(country = fct_reorder(country, median)) |>
  plot_ly(x = ~country, y = ~median, type = 'scatter', mode = 'markers') |> 
  layout(title = 'HIV Prevalence Rates by Country',
         yaxis = list(title = 'Prevalence Rate(%)'))
prev_country
# HIV Prevalence and Incidence Rates of Each Subpopulation Over Time

year_subpop_inc_plot = incidence |>
  group_by(subpop_pooled,year) |>
  summarise(median = median(inc_rate)) |>
  plot_ly(x = ~year, y = ~median, color = ~subpop_pooled, type = 'scatter', mode = 'lines') |>
    layout(title = 'HIV Incidence Rates of Each Subpopulation Over Year')
## `summarise()` has grouped output by 'subpop_pooled'. You can override using the
## `.groups` argument.
print(year_subpop_inc_plot)

Tables

# Load necessary libraries
library(dplyr)
library(knitr)
# Top 10 countries with the highest incidence

top_inc = incidence |>
  group_by(country) |>
  summarize(median = median(inc_rate)) |>
  arrange(desc(median)) |>
  top_n(10)
## Selecting by median
# Top 10 countries with the highest prevalence

top_prev = prevalence |>
  group_by(country) |>
  summarize(median = median(prev_rate)) |>
  arrange(desc(median)) |>
  top_n(10)
## Selecting by median
# Display the tables with kable for a professional look

kable(top_inc, caption = "Top 10 Countries with Highest HIV Incidence Rates", 
      format = "html", table.attr = "style='width:100%;'", 
      col.names = c("Country", "Median Incidence Rate"))
Top 10 Countries with Highest HIV Incidence Rates
Country Median Incidence Rate
Estonia 26.100
Ukraine 24.760
Israel 18.290
Nicaragua 14.400
Pakistan 12.450
Burma 10.100
Mexico 9.400
Uruguay 8.165
Russia 7.200
Gambia, The 6.925
kable(top_prev, caption = "Top 10 Countries with Highest HIV Prevalence Rates", 
      format = "html", table.attr = "style='width:100%;'", 
      col.names = c("Country", "Median Prevalence Rate"))
Top 10 Countries with Highest HIV Prevalence Rates
Country Median Prevalence Rate
Qatar 38.460
Eswatini 29.755
Burundi 29.355
Zambia 21.070
Botswana 20.875
Mauritius 19.780
Zimbabwe 19.290
Malawi 18.950
Uzbekistan 18.200
Guyana 17.765